A digital network analyzer (DNA) generally describes a network analyzer architecture that generates a digital stimulus pattern for system identification and a corresponding receiver architecture that recovers the system response. The DNA architecture also mixes the response with a digital mixing pattern, chosen so that mixing products of interest lie within a narrow intermediate frequency (IF). This enables the receiver architecture to measure broadband energy (resulting from broadband stimulus) using a commodity analog-to-digital converter (ADC), without requiring swept local oscillators (LO), for example. Since the broadband stimulus is acquired concurrently, rather than sequentially as with a swept LO, the DNA may be orders of magnitude faster than a traditional (swept LO) network analyzer. Further, the DNA is able to trade signal-to-noise ratio (SNR) for measurement time to a great extent and avoids LO settling times, band crossings, and the like.
The DNA receiver architecture captures a frequency compressed and frequency scrambled version of the broadband stimulus signal, which must be descrambled to recover the measurement. “Descrambling” means two things. First, one must identify the frequencies of interest. Typically, only 10 percent or fewer of captured frequencies are actually used in the measurement. Second, one must return the identified frequencies of interest to the original order as they appeared in the stimulus signal. However, conventional methods for deciphering the scrambled version of the broadband stimulus signal are orders of magnitude slower than the time required to acquire the broadband stimulus signal, as reflected by traces 120 and 130 in FIG. 1. This is primarily because conventional descrambling algorithms are implemented in the frequency domain. More specifically, the process includes accumulating one or more periods of the waveform of an IF signal, coherently averaging (also known as bin and average operation) the accumulated periods to improve SNR, performing a fast Fourier transform (FFT) on the coherently averaged IF signal, and extracting frequencies and phases of the frequency bins of interest. For example, extracting the frequencies and phases of the frequency bins of interest may use a special indexing function to identify locations of the frequency bins of interest (since the frequency bins of interest have been scrambled during the IF mixing operation).
The conventional descrambling algorithm tends to be time consuming. This is because the digital stimulus pattern of the RF signal, and consequently the period(s) of the IF signal, can become rather long, particularly for fine frequency resolutions. The period of the IF signal may be millions of samples in length. As, the FFT is performed on the entire captured waveform, the processing time can be significantly slower than the acquisition time. Also, transferring such a long record to a host computer (e.g., personal computer) takes time. The capture memory for performing the coherent averaging also must be sufficiently deep. The number of samples processed with this approach is proportional to the reciprocal of the measurement's frequency resolution, even for narrow measurement spans. Unfortunately, because of the scrambling introduced by the mixing process, there has been no efficient way to selectively compute just the frequency bins of interest. The number of samples in the IF period can be 10 times, 100 times or even more, the number of frequency bins that are actually retained from the FFT. Typically, though, the data transfer time and the digital signal processing (DSP) time are much longer than the acquisition time.
For example, FIG. 1 is a graph depicting time versus frequency resolution of various functions of a conventional DNA. Trace 110 of FIG. 1 shows acquisition time (Tacq) that it takes to acquire the digitized IF signal data (e.g., corresponding to period of the IF waveform), not including signal processing time. As would be expected, the time for IF signal data acquisition decreases as the desired frequency resolution decreases. Trace 120 shows the acquisition time (Tacq) plus transfer time (Ttransfer), which is the time required for the acquired IF signal data to be stored in capture memory and transferred to an FFT module for DSP. Trace 130 shows the acquisition time (Tacq) and the transfer time (Ttransfer) plus FFT time (Tdsp), which is the time required to perform FFTs on the IF signal data stored in capture memory. As a practical matter, the trace 130 represents the full amount time to complete a measurement using the conventional DNA. Trace 140, provided for purposes of comparison, shows measurement time of a conventional, very fast vector network analyzer (VNA), which performs measurements at a constant speed, responsive to the sweep time of the LO (as opposed to the period of the IF signal waveform). As shown in FIG. 1, if the DNA's measurement time were reduced to effectively match the acquisition time (Tacq), e.g., by reducing the transfer time (Ttransfer) and the FFT time (Tdsp), it would be significantly faster than the conventional VNA, particularly as frequency resolution decreases.
Accordingly, there is a need for a solution capable of accelerating the IF signal data transfer and processing functions, including descrambling operations, such that DNA measurements may be limited primarily by the data acquisition speed. This would further enable DNA measurements to be obtained and observed in real-time or near real-time.